Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art

Reference Nodes Selection for Anchor-Free Localization in Wireless Sensor Networks

Dizertační práce se zabývá návrhem nového bezkotevního lokalizačního algoritmu sloužícího pro výpočet pozice uzlů v bezdrátových senzorových sítích. Provedené studie ukázaly, že dosavadní bezkotevní lokalizační algoritmy, pracující v paralelním režimu, dosahují malých lokalizačních chyb. Jejich nevýhodou ovšem je, že při sestavení množiny referenčních uzlu spotřebovávají daleko větší množství energie než algoritmy pracující v inkrementálním režimu. Paralelní lokalizační algoritmy využívají pro určení pozice referenční uzly nacházející se na protilehlých hranách bezdrátové sítě. Nový lokalizační algoritmus označený jako BRL (Boundary Recognition aided Localization) je založen na myšlence decentralizovaně detekovat uzly ležící na hranici síti a pouze z této množiny vybrat potřebný počet referenčních uzlu. Pomocí navrženého přístupu lze znažně snížit množství energie spotřebované v průběhu procesu výběru referenčních uzlů v senzorovém poli. Dalším přínosem ke snížení energetických nároku a zároveň zachování nízké lokalizační chyby je využití procesu multilaterace se třemi, eventuálně čtyřmi referenčními body. V rámci práce byly provedeny simulace několika dílčích algoritmu a jejich funkčnost byla ověřena experimentálně v reálné senzorové síti. Navržený algoritmus BRL byl porovnán z hlediska lokalizační chyby a počtu zpracovaných paketů s několika známými lokalizačními algoritmy. Výsledky simulací dokázaly, že navržený algoritmus představuje efektivní řešení pro přesnou a zároveň nízkoenergetickou lokalizaci uzlů v bezdrátových senzorových sítích.The doctoral thesis is focused on a design of a novel anchor free localization algorithm for wireless sensor networks. As introduction, the incremental and concurrent anchor free localization algorithms are presented and their performance is compared. It was found that contemporary anchor free localization algorithms working in the concurrent manner achieve a low localization error, but dissipate signicant energy reserves. A new Boundary Recognition Aided Localization algorithm presented in this thesis is based on an idea to recognize the nodes placed on the boundary of network and thus reduce the number of transmission realized during the reference nodes selection phase of the algorithm. For the position estimation, the algorithm employs the multilateration technique that work eectively with the low number of the reference nodes. Proposed algorithms are tested through the simulations and validated by the real experiment with the wireless sensor network. The novel Boundary Recognition Aided Localization algorithm is compared with the known algorithms in terms of localization error and the communication cost. The results show that the novel algorithm presents powerful solution for the anchor free localization.

Localization Algorithms of Underwater Wireless Sensor Networks: A Survey

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Localization by decreasing the impact of obstacles in wireless sensor networks

In sensor networks ,Localization techniques makes use of small number of reference nodes, whose locations are known in prior, and other nodes estimate their coordinate position from the messages they receive from the anchor nodes. Localization protocol can be divided into two categories: (i) range-based and (ii) range-free protocols. Range-based protocols depend on knowing the distance between the nodes. Where as, range-free protocols consider the contents of message sent from the anchor node to all other sensor node. Previous range-free based localization methods requires at least three anchor nodes ,whose positions already known ,in order to find the position of unknown sensor node and these methods might not guarantee for complete solution and an infeasible case could occur. The convex position estimation method takes the advantage of solving the above problem. Here different approach to solve the localization problem is described. In which it considers a single moving anchor node and each node will have a set of mobile anchor node co-ordinates. Later this algorithm checks for the connectivity between the nodes to formulate the radical constraints and finds the unknown sensor node location. The nodes position obtained using convex position estimation method will have less location error. However, Network with obstacles is most common. Localizing these networks, some nodes may have higher location error. The new method is described to decrease the impact of obstacle, in which nodes near or within the obstacle that fail to get minimum of three anchor node position values get the anchor position set from its neighbor nodes, applies the convex position estimation method and gets localized with better position accuracy. The Convex position estimation method is range-free that solves localization problem when infeasible case occurs and results in better location accuracy

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size

SALAM: A SCALABLE ANCHOR-FREE LOCALIZATION ALGORITHM FOR WIRELESS SENSOR NETWORKS

In this dissertation, we present SALAM, a scalable anchor-free protocol for localization in wireless sensor networks. SALAM can determine the positions of sensor nodes without any infrastructure support. We assume that each node has the capability to estimate distances to its corresponding neighbors, that are within its transmission range. SALAM allows to trade the accuracy of the estimated position against node transmission range and/or computational power. The application layer can choose from a whole range of different options, to estimate the sensor node's positions with different accuracy while conserving battery power. Scalability is achieved by dividing the network into overlapping multi-hop clusters each with its own cluster head node. Each cluster head is responsible for building a local relative map corresponding to its cluster using intra-cluster node's range measurements. To obtain the global relative topology of the network, the cluster head nodes collaboratively combine their local maps using simple matrix transformations. In order for two cluster heads to perform a matrix transformation, there must be at least three boundary nodes that belongs to both clusters (i.e. the two clusters are overlapping with degree 3). We formulate the overlapping multi-hop clustering problem and present a randomized distributed heuristic algorithm for solving the problem. We evaluate the performance of the proposed algorithm through analytical analysis and simulation. A major problem with multi-hop relative location estimation is the error accumulated in the node position as it becomes multi-hop away from the cluster head node. We analyze different sources of error and develop techniques to avoid these errors. We also show how the local coordinate system (LCS) affects the accuracy and propose different heuristics to select the LCS. Simulation results show that SALAM achieves precise localization of sensors. We show that our approach is scalable in terms of communication overhead regardless of the network size. In addition, we capture the impact of different parameters on the accuracy of the estimated node's positions. The results also show that SALAM is able to achieve accuracy better than the current ad-hoc localization algorithms

Accurate Anchor-Free Node Localization in Wireless Sensor Networks

There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in such network setups in order to correlate the reported data to the origin of the sensed phenomena. In addition, awareness of the nodes’ positions can enable employing efficient management strategies such as geographic routing and conducting important analysis such as node coverage properties. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins. In addition, we capture the effect of error accumulation of the node’s range estimates and network’s size and connectivity on the overall accuracy of the unified coordinate system

Localization and security algorithms for wireless sensor networks and the usage of signals of opportunity

In this dissertation we consider the problem of localization of wireless devices in environments and applications where GPS (Global Positioning System) is not a viable option. The _x000C_rst part of the dissertation studies a novel positioning system based on narrowband radio frequency (RF) signals of opportunity, and develops near optimum estimation algorithms for localization of a mobile receiver. It is assumed that a reference receiver (RR) with known position is available to aid with the positioning of the mobile receiver (MR). The new positioning system is reminiscent of GPS and involves two similar estimation problems. The _x000C_rst is localization using estimates of time-di_x000B_erence of arrival (TDOA). The second is TDOA estimation based on the received narrowband signals at the RR and the MR. In both cases near optimum estimation algorithms are developed in the sense of maximum likelihood estimation (MLE) under some mild assumptions, and both algorithms compute approximate MLEs in the form of a weighted least-squares (WLS) solution. The proposed positioning system is illustrated with simulation studies based on FM radio signals. The numerical results show that the position errors are comparable to those of other positioning systems, including GPS. Next, we present a novel algorithm for localization of wireless sensor networks (WSNs) called distributed randomized gradient descent (DRGD), and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that blind nodes be contained in the convex hull of the anchor nodes, and can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely the relaxation-based second order cone programming (SOCP), the simulated annealing (SA), and the semi-de_x000C_nite programing (SDP) procedures. Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. We also present a modi_x000C_cation of DRGD for mobile WSNs and demonstrate the e_x000E_cacy of DRGD for localization of mobile networks with several simulation results. We then extend this method for secure localization in the presence of outlier distance measurements or distance spoo_x000C_ng attacks. In this case we present a centralized algorithm to estimate the position of the nodes in WSNs, where outlier distance measurements may be present